III: Small: Quantifying Multifaceted Perception Dynamics in Online Social Networks

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Measuring public perceptions and how they change over time is a central problem in marketing, public health, and politics. Traditional measurement methods rely on surveys and focus groups, which can be costly and time-consuming. Online social networks offer an attractive alternative: real-time perceptions can be estimated from public, online activity and compared with an entity's communications to quantify how public messaging affects perception. While prior algorithmic approaches rely purely on text-based sentiment analysis, this project will develop novel methods based on the insight that an entity's online social connections are indicative of how they are perceived (e.g., "birds of a feather flock together"). Thus, rather than typical one-dimensional measures of sentiment, the project will instead investigate public perception with respect to multiple characteristics of an entity (e.g., is it seen as pro-environment, pro-health, etc.). A multi-faceted evaluation will be performed to study the phenomenon of "greenwashing," a deceptive marketing practice in which firms market their products or policies as more environmentally friendly than they truly are. This project has the potential to enhance consumer protection by exposing deceptive marketing practices.The project will develop social network analysis algorithms to assess perception of an entity and also language processing algorithms to quantify the communications of an entity with respect to a perceptual attribute. The approaches to both problems rely on innovative algorithms to measure the strengths of the social and linguistic relations between public entities and exemplar accounts that typify the perceptual attribute of interest. A key advantage of the approach is its minimal requirement of human input, e.g., given only a single keyword like "environment," the approach identifies suitable exemplars and fits linguistic and perceptual models. The project will develop novel machine learning methods for domain adaptation, positive-unlabeled learning, and learning from label proportions in order to fit such models and ensure they are robust to omitted variable bias. The models will be evaluated using public Twitter and Facebook data to quantify the relationship between the perceptions and online communications of brands and other public entities, with a particular focus on identifying cases of greenwashing.